Chun-Kit Ngan

Worcester Polytechnic Institute

Dr. Chun-Kit Ngan is an Assistant Teaching Professor of Data Science in the Department of Computer Science at Worcester Polytechnic Institute (WPI) since January 2018. Before joining WPI, he was an Assistant Professor in the Division of Engineering and Information Science at the Pennsylvania State University-Great Valley (PSU-GV). His research interests are Decision Guidance and Support Systems (DGSS), including Decision Optimization Models, Deep/Machine Learning, Computational Algorithms, Natural Language Processing, Data Analytics and Visualization, and DGSS Applications, to guide domain-specific decision makers to make better decisions and provide them with actionable recommendations. He has published over 20 articles in various books, journals, and conferences. He received the Best Paper Award and the Best Student Paper Award at the 2013 and 2011 International Conference on Enterprise Information Systems respectively. His co-authored paper was selected as the Best Presentation Award in the Information Theory and Technology Session at the 2019 International Conference on Information System and Data Mining. He was the recipient of the 2013-2014 Seed Money Research Grant and 2015-2016 Early Career Award for Research and Scholarship Excellence at PSU-GV. He is also the Co-PI of the funded 2019-2022 NSF REU Site \"Data Science for Healthy Communities in the Digital Age\" at WPI.

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Latest work with IntechOpen by Chun-Kit Ngan

This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. This book is divided into three sections and each section includes two chapters. Section 1 discusses analyzing multivariate and fuzzy time series. Section 2 focuses on developing deep neural networks for time series forecasting and classification. Section 3 describes solving real-world domain-specific problems using time series techniques. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.

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